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      "\r",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Current iteration: 100%|██████████| 1239/1239 [1:40:07<00:00,  4.85s/it]\u001b[A\n",
      "Epoch: 100%|██████████| 1/1 [1:40:07<00:00, 6007.57s/it]\n",
      "  0%|          | 0/66 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training of roberta model complete. Saved to outputs/.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 66/66 [01:19<00:00,  1.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'mcc': 0.8546426396822812, 'tp': 240, 'tn': 244, 'fp': 16, 'fn': 22}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import argparse\n",
    "import math\n",
    "import pandas as pd\n",
    "from simpletransformers.model import TransformerModel\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from alibi.datasets import fetch_movie_sentiment\n",
    "\n",
    "def prepare_data(test_size):\n",
    "    # load data\n",
    "    X, y = fetch_movie_sentiment(return_X_y=True)\n",
    "\n",
    "    # prepare data\n",
    "    data = pd.DataFrame()\n",
    "    data['text'] = X\n",
    "    data['labels'] = y\n",
    "\n",
    "    if math.isclose(test_size, 0.0):\n",
    "        return data, None\n",
    "    else:\n",
    "        train, test = train_test_split(data, test_size=test_size)\n",
    "        return train, test\n",
    "\n",
    "\n",
    "train, test = prepare_data(0.20)\n",
    "model = TransformerModel('roberta', 'roberta-base', args=({'fp16': False}))\n",
    "model.train_model(train)\n",
    "\n",
    "\n",
    "result, model_outputs, wrong_predictions = model.eval_model(test)\n",
    "    \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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